It looks like Raustats might not be what I am looking for to get SLA level census data quickly. Let’s try Census2016 from Hugh Parsonage.

rr full_2016_census <- Census2016_wide_by_SA2_year %>% filter(year == ‘2016’ ) head(full_2016_census)

Yes - this is what I need.

Loading other tables

rr ancestories_2016 <- Census2016_ancestories %>% filter(year == ‘2016’ ) countries_of_birth_2016 <- Census2016_countries_of_birth %>% filter(year == ‘2016’ ) languages_2016 <- Census2016_languages %>% filter(year == ‘2016’ ) religions_2016 <- Census2016_religions %>% filter(year == ‘2016’ )

Feature Engineering

Each of the four variables ancestory, country of birth, languages and religions are quite granular, and it may make sense to look at these variables at a lower level of granularity.

Ancestory:

rr download.file(‘https://www.abs.gov.au/AUSSTATS/subscriber.nsf/log?openagent&12490do0001_201912.xls&1249.0&Data%20Cubes&674EFC4CA0A3D8FDCA2584D30012B905&0&2019&18.12.2019&Latest’, ‘./Data/ancestry_classification.xls’, method = ‘libcurl’)

ancestry_classification_4dig <- readxl::read_xls(‘./Data/ancestry_classification.xls’, sheet = ‘Table 1.3’, skip = 7, col_names = c(‘X1’, ‘X2’, ‘Ancestory_Code_4’, ‘Ancestory’)) %>% filter(!is.na(Ancestory)) %>% select(Ancestory_Code_4, Ancestory)

ancestry_classification_1dig <- readxl::read_xls(‘./Data/ancestry_classification.xls’, sheet = ‘Table 1.1’, skip = 5, col_names = c(‘Ancestory_Code_1’, ‘Ancestory_Group’))%>% filter(!is.na(Ancestory_Group))

Country of birth

rr download.file(‘https://www.abs.gov.au/ausstats/subscriber.nsf/log?openagent&sacc_12690do0001_201903.xls&1269.0&Data%20Cubes&480BD730AF42D515CA2583BD007707C5&0&2016&15.03.2019&Latest’, ‘./Data/country_classification.xls’, method = ‘libcurl’)

country_classification_4dig <- readxl::read_xls(‘./Data/country_classification.xls’, sheet = ‘Table 1.3’, skip = 7, col_names = c(‘X1’, ‘X2’, ‘Country_Code_4’, ‘Country’)) %>% filter(!is.na(Country)) %>% select(-X1, -X2)

country_classification_2dig <- readxl::read_xls(‘./Data/country_classification.xls’, sheet = ‘Table 1.2’, skip = 6, col_names = c(‘X1’, ‘Country_Code_2’, ‘Country_Name_2’)) %>% filter(!is.na(Country_Name_2)) %>% select(-X1)

country_classification_1dig <- readxl::read_xls(‘./Data/country_classification.xls’, sheet = ‘Table 1.1’, skip = 5, col_names = c(‘Country_Code_1’, ‘Country_Group’)) %>% filter(!is.na(Country_Group))

Language

rr download.file(‘https://www.abs.gov.au/AUSSTATS/subscriber.nsf/log?openagent&ASCL_12670DO0001_201703.xls&1267.0&Data%20Cubes&F84620CF6E13F7E8CA257FF1001E68A7&0&2016&28.03.2017&Latest’, ‘./Data/language_classification.xls’, method = ‘libcurl’)

language_classification_4dig <- readxl::read_xls(‘./Data/language_classification.xls’, sheet = ‘Table 1.3’, skip = 8, col_names = c(‘X1’, ‘X2’, ‘X3’, ‘X4’, ‘Language_Code_3’, ‘Language’)) %>% filter(!is.na(Language)) %>% select(-X1, -X2, -X3, -X4)

language_classification_1dig <- readxl::read_xls(‘./Data/language_classification.xls’, sheet = ‘Table 1.1’, skip = 5, col_names = c(‘Language_Code_1’, ‘Language_Group’)) %>% filter(!is.na(Language_Group))

Religion

rr download.file(‘https://www.abs.gov.au/AUSSTATS/subscriber.nsf/log?openagent&ASCRG_12660DO0001_201707.xls&1266.0&Data%20Cubes&B3EAFE3FE6180D37CA257FF1001E673C&0&2016&14.07.2017&Latest’, ‘./Data/religion_classification.xls’, method = ‘libcurl’)

religion_classification_3dig <- readxl::read_xls(‘./Data/religion_classification.xls’, sheet = ‘Table 1.2’, skip = 6, col_names = c(‘X1’, ‘Religion_Code_3’, ‘Religion’)) %>% filter(!is.na(Religion)) %>% select(-X1)

religion_classification_1dig <- readxl::read_xls(‘./Data/religion_classification.xls’, sheet = ‘Table 1.1’, skip = 5, col_names = c(‘Religion_Code_1’, ‘Religion_Group’)) %>% filter(!is.na(Religion_Group))

Combine with election data

Aggregate at the SA2 level - add unless variable contains median, average, persons_per_bedroom

census_2016_all_vars <- Census2016_wide_by_SA2_year %>% 
  filter(year == '2016') %>% 
  rowwise() %>% 
  mutate(sa2_id = paste0(substr(sa2_code, 1, 1), substr(sa2_code, 6, 9))) %>% 
  filter(isMissing == FALSE) %>% 
  mutate(percent_female = female/persons,
         percent_defacto = defacto_persons/persons,
         percent_married = married_persons/persons,
         percent_indig = indig_persons/persons,
         percent_born_in_australia = born_in_australia/persons,
         percent_unit = flat_or_unit/n_dwellings,
         percent_mortgage = dwelling_owned_mortgage/n_dwellings,
         percent_rent = dwelling_rented/n_dwellings)


census_2016_means <- census_2016_all_vars %>% 
  select(median_age, median_household_income, average_household_size, 
         persons_per_bedroom, median_weekly_rent, median_annual_mortgage, sa2_id) %>% 
  group_by(sa2_id) %>% 
  summarise_all(mean, na.rm = TRUE)

census_2016_counts <- census_2016_all_vars %>% 
  select(n_dwellings, persons, female, male, 
         married_persons, married_females, married_males, defacto_persons, 
         defacto_females, defacto_males, notmarried_persons, 
         notmarried_females, notmarried_males, indig_persons, 
         indig_males, indig_females, non_indig_persons, 
         non_indig_females, non_indig_males, not_stated_indig_persons, 
         not_stated_indig_males, not_stated_indig_females, 
         born_in_australia, born_overseas, country_not_stated, 
         separate_house, flat_or_unit, housing_other_or_not_stated, semi_or_townhouse, 
         dwelling_owned_outright, dwelling_owned_mortgage, dwelling_other_or_not_stated,
         dwelling_rented, sa2_id) %>% 
  group_by(sa2_id) %>% 
  summarise_all(sum, na.rm = TRUE) %>% 
  mutate(percent_female = female/persons,
         percent_defacto = defacto_persons/persons,
         percent_married = married_persons/persons,
         percent_indig = indig_persons/persons,
         percent_born_in_australia = born_in_australia/persons,
         percent_unit = flat_or_unit/n_dwellings,
         percent_mortgage = dwelling_owned_mortgage/n_dwellings,
         percent_rent = dwelling_rented/n_dwellings)

So what I need is weighted demographic data for each of the polling places based on the number of people from each SLA2 who voted at the polling place. Since we don’t know who voted where, and who can vote at all, we are making the naive assumptions that * Voters at each SLA are similar * Voters are representitive of census respondents at the SLA2 level.

Download and load polling places by SA1


download.file('https://www.aec.gov.au/Elections/Federal_Elections/2016/files/polling-place-by-sa1s-2016.xlsx', './Data/polling-place-by-sa1s-2016.xlsx', method = 'libcurl')
trying URL 'https://www.aec.gov.au/Elections/Federal_Elections/2016/files/polling-place-by-sa1s-2016.xlsx'
Content type 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet' length 31087463 bytes (29.6 MB)
==================================================
downloaded 29.6 MB
polling_place_data <- readxl::read_xlsx('./Data/polling-place-by-sa1s-2016.xlsx')

Aggregate polling place data to SA2

rr polling_place_sa2 <- polling_place_data %>% mutate(sa2_id = floor(SA1_id / 100)) %>% group_by(year, state_ab, div_nm, pp_id, pp_nm, sa2_id) %>% summarise(votes = sum(votes))

Combine with demographic data and aggregate

polling_place_demog <- polling_place_sa2 %>% 
  mutate(sa2_id = as.character(sa2_id)) %>% 
  inner_join(census_2016_all_vars)

polling_place_demog_means <- polling_place_demog %>% 
  select(year, state_ab, div_nm, pp_id, pp_nm, sa2_id, votes,
         median_age, median_household_income, average_household_size, 
         persons_per_bedroom, median_weekly_rent, median_annual_mortgage,
         percent_female, percent_defacto, percent_married, percent_indig, 
         percent_born_in_australia, percent_unit, percent_mortgage, percent_rent) %>% 
  group_by(year, state_ab, div_nm, pp_id, pp_nm) %>% 
  summarise_at(vars(median_age, median_household_income, 
                    average_household_size, persons_per_bedroom, median_weekly_rent, 
                    median_annual_mortgage, percent_female, percent_defacto, 
                    percent_married, percent_indig, percent_born_in_australia,
                    percent_unit, percent_mortgage, 
                    percent_rent), funs(weighted.mean(., w=votes)))

Add in 2pp at the polling booth level

rr election_2pp <- twoparty_pollingbooth_download()

trying URL 'https://github.com/ropenscilabs/eechidna/raw/master/extra-data/tpp_pp.rda'
Content type 'application/octet-stream' length 1677810 bytes (1.6 MB)
==================================================
downloaded 1.6 MB

rr polling_place_2pp <- polling_place_demog_means %>% group_by() %>% rename(StateAb = state_ab, DivisionNm = div_nm, PollingPlace = pp_nm) %>% mutate(DivisionNm = toupper(DivisionNm), PollingPlace = toupper(PollingPlace)) %>% left_join(election_2pp %>% filter(year == 2016))

Joining, by = c(\year\, \StateAb\, \DivisionNm\, \PollingPlace\)

Check for missing data

polling_place_2pp %>% 
  summarise_all(funs(sum(is.na(.))))

Which booths are null?

polling_place_2pp %>% 
  filter(is.na(TotalVotes)) %>% 
  tabyl(PollingPlace)
 PollingPlace   n percent
       ABSENT 150    0.25
       POSTAL 150    0.25
     PRE-POLL 150    0.25
  PROVISIONAL 150    0.25

So the Absent, Postals, Pre-Poll and Provisional votes aren’t in this table. Let’s come back to those…

polling_place_2pp %>% 
  summarise_all(funs(sum(is.null(.))))

Remove the rows with NAs

polling_place_2pp_clean <- polling_place_2pp %>% 
  filter(!is.na(TotalVotes))
polling_place_2pp_clean %>% 
  summarise_all(funs(sum(is.na(.))))

Which polling stations have missing data? Not too concerned about post code, as there are some special booths

polling_place_2pp_clean %>% 
  filter(is.na(median_age)|is.na(Latitude))

Looks like mobile teams and prepoll centres, and only latitude and longitude. Will remove the Brand mobile team, as the demographic data does not look valid.

polling_place_2pp_clean<- polling_place_2pp_clean %>% 
  dplyr::filter(PollingPlaceID != 65161)

Visualising Basic Statistics

polling_place_2pp_clean %>% 
  ggplot(aes(x = LNP_Percent/100)) + stat_density(geom="line", colour = 'blue') +
  theme_classic(base_size = 16) +
  scale_x_continuous(labels=scales::percent) +
  labs(title = '2016 Election: LNP 2 Party Preferred Percentage', x = '2PP Percentage', 
       y = 'Density', subtitle = 'by Polling Booth, Unweighted')

polling_place_2pp_clean %>% 
  ggplot(aes(x = ALP_Percent/100)) + stat_density(geom="line", colour = 'red') +
  theme_classic(base_size = 16) +
  scale_x_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', x = '2PP Percentage', 
       y = 'Density', subtitle = 'by Polling Booth, Unweighted')

polling_place_2pp_clean %>% 
  ggplot(aes(x = Swing/100)) + stat_density(geom="line", colour = 'purple') +
  theme_classic(base_size = 16) +
  scale_x_continuous(labels=scales::percent) +
  labs(title = '2016 Election: Swing to Incumbent', x = 'Swing', 
       y = 'Density', subtitle = 'by Polling Booth, Unweighted')

polling_place_2pp_clean %>% 
  ggplot(aes(x = median_household_income)) + stat_density(geom="line", colour = 'purple') +
  theme_classic(base_size = 16) +
  scale_x_continuous(labels=scales::dollar) +
  labs(title = '2016 Census: Median Income', x = 'Median Income', 
       y = 'Density', subtitle = 'by Polling Booth, Unweighted')

State Breakdowns

Can we look at these distributions by state?

polling_place_2pp_clean %>% 
  ggplot(aes(x = ALP_Percent/100, colour = StateAb)) + 
  stat_density(geom="line", position = 'dodge') +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  scale_x_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', x = '2PP Percentage', 
       y = 'Frequency', subtitle = 'by Polling Booth, Unweighted')

What about by median income

polling_place_2pp_clean %>%
  ggplot(aes(y = ALP_Percent/100, x = median_household_income, colour = StateAb)) +
  geom_point() +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::dollar) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', x = 'Booth Median Income',
       y = 'ALP 2pp Percentage', subtitle = 'by Polling Booth, Unweighted') +
  facet_wrap(~StateAb, nrow = 4)

What about comparing NSW electorates? There seems to be an odd separation in income bands for low ALP 2pp. Could this be a regional vs city difference?

fp_booth_16 <- firstpref_pollingbooth_download() %>% 
  filter(year == 2016)
trying URL 'https://github.com/ropenscilabs/eechidna/raw/master/extra-data/fp_pp.rda'
Content type 'application/octet-stream' length 3227934 bytes (3.1 MB)
==================================================
downloaded 3.1 MB
polling_2cp <- read_csv('https://results.aec.gov.au/20499/Website/Downloads/HouseTcpByCandidateByPollingPlaceDownload-20499.csv', skip = 1)

polling_2pp <- read_csv('https://results.aec.gov.au/20499/Website/Downloads/HouseTppByPollingPlaceDownload-20499.csv', skip = 1)

fp_booth_2016 <- read_csv('https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-NSW.csv', skip = 1) %>% 
  rbind(read_csv('https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-VIC.csv', skip = 1)) %>% 
  rbind(read_csv('https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-QLD.csv', skip = 1)) %>% 
  rbind(read_csv('https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-SA.csv', skip = 1)) %>% 
  rbind(read_csv('https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-WA.csv', skip = 1)) %>% 
  rbind(read_csv('https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-TAS.csv', skip = 1)) %>% 
  rbind(read_csv('https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-NT.csv', skip = 1)) %>% 
  rbind(read_csv('https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-ACT.csv', skip = 1))
coalition_contest_2016 <- fp_booth_2016 %>% 
  group_by(DivisionNm, PartyNm, HistoricElected) %>% 
  summarise(OrdinaryVotes = sum(OrdinaryVotes)) %>%
  filter(PartyNm %in% c('Liberal', 'Country Liberals (NT)',
                        'Liberal National Party of Queensland',
                        'The Nationals')) %>% 
  group_by(DivisionNm) %>% 
  top_n(1) %>% 
  select(DivisionNm, PartyNm)
Selecting by OrdinaryVotes

If we look at a couple of the states where high income booths tend to vote strongly for the coalition as well as lower income booths, we can see that some (but not all) of the lower income booths are contested by The Nationals. This indicares (not surprisingly) that Nationals voters and Liberal voters are different socio-economically, or possibly that city and country coalition voters differ.

polling_place_2pp_clean %>% 
  mutate(DivisionNm = stringr::str_to_title(DivisionNm)) %>%
  inner_join(coalition_contest_2016) %>% 
  filter(StateAb == 'NSW') %>% 
  ggplot(aes(y = ALP_Percent/100, x = median_household_income, colour = PartyNm)) +
  geom_point(size = 3) +
  theme_classic(base_size = 16) + scale_color_manual(values = c('blue', 'dark green')) +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::dollar) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', x = 'Booth Median Income',
       y = 'ALP 2pp Percentage', colour = 'Coalition Party', 
       subtitle = 'by Polling Booth, Unweighted (NSW)') 

polling_place_2pp_clean %>% 
  mutate(DivisionNm = stringr::str_to_title(DivisionNm)) %>%
  inner_join(coalition_contest_2016) %>% 
  filter(StateAb == 'VIC') %>% 
  ggplot(aes(y = ALP_Percent/100, x = median_household_income, colour = PartyNm)) +
  geom_point(size = 3) +
  theme_classic(base_size = 16) + scale_color_manual(values = c('blue', 'dark green')) +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::dollar) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', x = 'Booth Median Income',
       y = 'ALP 2pp Percentage', colour = 'Coalition Party', 
       subtitle = 'by Polling Booth, Unweighted (VIC)') 

This effect is less clear in states where the Nationals aren’t as prominent, either because the Nationals aren’t as prominent (SA, WA, TAS), or are merged with the Liberals (QLD).

polling_place_2pp_clean %>% 
  mutate(DivisionNm = stringr::str_to_title(DivisionNm)) %>%
  inner_join(coalition_contest_2016) %>% 
  filter(StateAb == 'WA') %>% 
  ggplot(aes(y = ALP_Percent/100, x = median_household_income, colour = PartyNm)) +
  geom_point(size = 3) +
  theme_classic(base_size = 16) + scale_color_manual(values = c('blue', 'dark green')) +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::dollar) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', x = 'Booth Median Income',
       y = 'ALP 2pp Percentage', colour = 'Coalition Party', 
       subtitle = 'by Polling Booth, Unweighted (WA)') 

Perhaps we would be better off using the geographical classifications from the AEC.

library(rvest)

webpage <- read_html("http://results.aec.gov.au/20499/Website/HouseDivisionClassifications-20499-NAT.htm")

Division_Classifications <- webpage %>%
  html_nodes("#divisionClassifications") %>% 
  html_table(fill = TRUE) %>%
  .[[1]]
Division_Classifications <- Division_Classifications %>% 
  filter(Division != 'Total Enrolment')

The graph below shows that the booths that have a low ALP 2pp and a low median income are primarily rural booths. This relationship seems stronger than the Lib/Nat split.

polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  filter(StateAb == 'NSW') %>% 
  ggplot(aes(y = ALP_Percent/100, x = median_household_income, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::dollar) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', 
       y = 'ALP 2pp Percentage',
       x = 'Booth Median Income', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted (NSW)')

Looking at all states we see a similar relationship, although less strong than in NSW.

polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  ggplot(aes(y = ALP_Percent/100, x = median_household_income, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::dollar) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', y = 'ALP 2pp Percentage',
       x = 'Median Booth Income', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted')

What about some of the other variables?

polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  ggplot(aes(y = ALP_Percent/100, x = average_household_size, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', y = 'ALP 2pp Percentage',
       x = 'Average Household Size', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted')

polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  ggplot(aes(y = ALP_Percent/100, x = percent_female, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_x_continuous(labels=scales::percent) +
  scale_y_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', y = 'ALP 2pp Percentage',
       x = 'Percent Female', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted')

polling_place_2pp_clean %>% 
  select(ALP_Percent, Swing, median_age, median_household_income, average_household_size,
         persons_per_bedroom, median_weekly_rent, median_annual_mortgage,
         percent_female, percent_defacto, percent_born_in_australia,
         percent_unit, percent_mortgage, percent_rent) %>% 
  cor %>% 
  kable()
ALP_Percent Swing median_age median_household_income average_household_size persons_per_bedroom median_weekly_rent median_annual_mortgage percent_female percent_defacto percent_born_in_australia percent_unit percent_mortgage percent_rent
ALP_Percent 1.0000000 -0.2924398 -0.4148188 0.0049899 0.1785014 0.3698085 0.1675435 0.1230552 0.1245266 0.1270139 -0.3301750 NA NA NA
Swing -0.2924398 1.0000000 0.0592447 0.1044271 -0.0872137 0.0165807 0.0817794 0.0964162 0.0734776 0.0042180 -0.0448560 NA NA NA
median_age -0.4148188 0.0592447 1.0000000 -0.4804955 -0.5175758 -0.5943939 -0.4116337 -0.4221270 -0.0574141 -0.0502159 0.5197535 NA NA NA
median_household_income 0.0049899 0.1044271 -0.4804955 1.0000000 0.4183950 0.3362658 0.8029106 0.8644605 0.1982974 -0.0693950 -0.3959055 NA NA NA
average_household_size 0.1785014 -0.0872137 -0.5175758 0.4183950 1.0000000 0.3887206 0.3408666 0.3284517 -0.0032833 -0.5164718 -0.3020229 NA NA NA
persons_per_bedroom 0.3698085 0.0165807 -0.5943939 0.3362658 0.3887206 1.0000000 0.4120754 0.4015767 0.0035249 0.0094906 -0.5938012 NA NA NA
median_weekly_rent 0.1675435 0.0817794 -0.4116337 0.8029106 0.3408666 0.4120754 1.0000000 0.9276979 0.4307971 -0.1337758 -0.6013994 NA NA NA
median_annual_mortgage 0.1230552 0.0964162 -0.4221270 0.8644605 0.3284517 0.4015767 0.9276979 1.0000000 0.3628219 -0.1183373 -0.5499270 NA NA NA
percent_female 0.1245266 0.0734776 -0.0574141 0.1982974 -0.0032833 0.0035249 0.4307971 0.3628219 1.0000000 -0.1010107 -0.1145354 NA NA NA
percent_defacto 0.1270139 0.0042180 -0.0502159 -0.0693950 -0.5164718 0.0094906 -0.1337758 -0.1183373 -0.1010107 1.0000000 0.2383005 NA NA NA
percent_born_in_australia -0.3301750 -0.0448560 0.5197535 -0.3959055 -0.3020229 -0.5938012 -0.6013994 -0.5499270 -0.1145354 0.2383005 1.0000000 NA NA NA
percent_unit NA NA NA NA NA NA NA NA NA NA NA 1 NA NA
percent_mortgage NA NA NA NA NA NA NA NA NA NA NA NA 1 NA
percent_rent NA NA NA NA NA NA NA NA NA NA NA NA NA 1
polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  ggplot(aes(y = ALP_Percent/100, x = persons_per_bedroom, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', y = 'ALP 2pp Percentage',
       x = 'Persons per Bedroom', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted')

polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  ggplot(aes(y = ALP_Percent/100, x = percent_unit, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', y = 'ALP 2pp Percentage',
       x = 'Percent of Dwellings - Unit', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted')

polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  ggplot(aes(y = ALP_Percent/100, x = percent_mortgage, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', y = 'ALP 2pp Percentage',
       x = 'Percent of Dwellings - Under Mortgage', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted')

polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  ggplot(aes(y = ALP_Percent/100, x = percent_rent, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', y = 'ALP 2pp Percentage',
       x = 'Percent of Dwellings - Renting', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted')

polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  ggplot(aes(y = ALP_Percent/100, x = percent_indig, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', y = 'ALP 2pp Percentage',
       x = 'Percent Indigeneous', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted')

polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  ggplot(aes(y = ALP_Percent/100, x = percent_born_in_australia, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', y = 'ALP 2pp Percentage',
       x = 'Percent Born in Australia', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted')

polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  ggplot(aes(y = ALP_Percent/100, x = percent_defacto, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', y = 'ALP 2pp Percentage',
       x = 'Percent in a Defacto Relationship', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted')

---
title: "Explore Census2016 Package"
output: html_notebook
---

It looks like Raustats might not be what I am looking for to get SLA level census data quickly. Let's try Census2016 from Hugh Parsonage.

```{r, warning=FALSE, echo=FALSE, error=FALSE, message=FALSE}
library(tidyverse)
library(Census2016)
library(eechidna)
library(RColorBrewer)
library(janitor)
library(kableExtra)
knitr::opts_chunk$set(warning=FALSE)
# knitr::opts_chunk$set(error=FALSE)
knitr::opts_chunk$set(message=FALSE)
```

```{r}
full_2016_census <- Census2016_wide_by_SA2_year %>% 
  filter(year == '2016' )
head(full_2016_census)
```

Yes - this is what I need.

Loading other tables

```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
ancestories_2016 <- Census2016_ancestories %>% 
  filter(year == '2016' )
countries_of_birth_2016 <- Census2016_countries_of_birth %>% 
  filter(year == '2016' )
languages_2016 <- Census2016_languages %>% 
  filter(year == '2016' )
religions_2016 <- Census2016_religions %>% 
  filter(year == '2016' )
```

# Feature Engineering

Each of the four variables ancestory, country of birth, languages and religions are quite granular, and it may make sense to look at these variables at a lower level of granularity. 

Ancestory:
```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
download.file('https://www.abs.gov.au/AUSSTATS/subscriber.nsf/log?openagent&12490do0001_201912.xls&1249.0&Data%20Cubes&674EFC4CA0A3D8FDCA2584D30012B905&0&2019&18.12.2019&Latest', './Data/ancestry_classification.xls', method = 'libcurl')

ancestry_classification_4dig <- 
  readxl::read_xls('./Data/ancestry_classification.xls', sheet = 'Table 1.3', 
                   skip = 7, col_names = c('X1', 'X2', 'Ancestory_Code_4', 'Ancestory')) %>% 
  filter(!is.na(Ancestory)) %>% 
  select(Ancestory_Code_4, Ancestory)

ancestry_classification_1dig <- 
  readxl::read_xls('./Data/ancestry_classification.xls', sheet = 'Table 1.1', 
                   skip = 5, col_names = c('Ancestory_Code_1', 'Ancestory_Group'))%>% 
  filter(!is.na(Ancestory_Group))
```

Country of birth
```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
download.file('https://www.abs.gov.au/ausstats/subscriber.nsf/log?openagent&sacc_12690do0001_201903.xls&1269.0&Data%20Cubes&480BD730AF42D515CA2583BD007707C5&0&2016&15.03.2019&Latest', './Data/country_classification.xls', method = 'libcurl')

country_classification_4dig <- 
  readxl::read_xls('./Data/country_classification.xls', sheet = 'Table 1.3', 
                   skip = 7, col_names = c('X1', 'X2', 'Country_Code_4', 'Country')) %>% 
  filter(!is.na(Country)) %>% 
  select(-X1, -X2)

country_classification_2dig <- 
  readxl::read_xls('./Data/country_classification.xls', sheet = 'Table 1.2', 
                   skip = 6, col_names = c('X1', 'Country_Code_2', 'Country_Name_2')) %>% 
  filter(!is.na(Country_Name_2)) %>% 
  select(-X1)

country_classification_1dig <- 
  readxl::read_xls('./Data/country_classification.xls', sheet = 'Table 1.1', 
                   skip = 5, col_names = c('Country_Code_1', 'Country_Group')) %>% 
  filter(!is.na(Country_Group))
```

Language
```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
download.file('https://www.abs.gov.au/AUSSTATS/subscriber.nsf/log?openagent&ASCL_12670DO0001_201703.xls&1267.0&Data%20Cubes&F84620CF6E13F7E8CA257FF1001E68A7&0&2016&28.03.2017&Latest', './Data/language_classification.xls', method = 'libcurl')

language_classification_4dig <- 
  readxl::read_xls('./Data/language_classification.xls', sheet = 'Table 1.3', 
                   skip = 8, col_names = c('X1', 'X2', 'X3', 'X4', 'Language_Code_3', 'Language')) %>% 
  filter(!is.na(Language)) %>% 
  select(-X1, -X2, -X3, -X4)

language_classification_1dig <- 
  readxl::read_xls('./Data/language_classification.xls', sheet = 'Table 1.1', 
                   skip = 5, col_names = c('Language_Code_1', 'Language_Group')) %>% 
  filter(!is.na(Language_Group))
```

Religion
```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
download.file('https://www.abs.gov.au/AUSSTATS/subscriber.nsf/log?openagent&ASCRG_12660DO0001_201707.xls&1266.0&Data%20Cubes&B3EAFE3FE6180D37CA257FF1001E673C&0&2016&14.07.2017&Latest', './Data/religion_classification.xls', method = 'libcurl')

religion_classification_3dig <- 
  readxl::read_xls('./Data/religion_classification.xls', sheet = 'Table 1.2', 
                   skip = 6, col_names = c('X1', 'Religion_Code_3', 'Religion')) %>% 
  filter(!is.na(Religion)) %>% 
  select(-X1)

religion_classification_1dig <- 
  readxl::read_xls('./Data/religion_classification.xls', sheet = 'Table 1.1', 
                   skip = 5, col_names = c('Religion_Code_1', 'Religion_Group')) %>% 
  filter(!is.na(Religion_Group))
```




# Combine with election data

Aggregate at the SA2 level - add unless variable contains median, average, persons_per_bedroom
```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
census_2016_all_vars <- Census2016_wide_by_SA2_year %>% 
  filter(year == '2016') %>% 
  rowwise() %>% 
  mutate(sa2_id = paste0(substr(sa2_code, 1, 1), substr(sa2_code, 6, 9))) %>% 
  filter(isMissing == FALSE) %>% 
  mutate(percent_female = female/persons,
         percent_defacto = defacto_persons/persons,
         percent_married = married_persons/persons,
         percent_indig = indig_persons/persons,
         percent_born_in_australia = born_in_australia/persons,
         percent_unit = flat_or_unit/n_dwellings,
         percent_mortgage = dwelling_owned_mortgage/n_dwellings,
         percent_rent = dwelling_rented/n_dwellings)


census_2016_means <- census_2016_all_vars %>% 
  select(median_age, median_household_income, average_household_size, 
         persons_per_bedroom, median_weekly_rent, median_annual_mortgage, sa2_id) %>% 
  group_by(sa2_id) %>% 
  summarise_all(mean, na.rm = TRUE)

census_2016_counts <- census_2016_all_vars %>% 
  select(n_dwellings, persons, female, male, 
         married_persons, married_females, married_males, defacto_persons, 
         defacto_females, defacto_males, notmarried_persons, 
         notmarried_females, notmarried_males, indig_persons, 
         indig_males, indig_females, non_indig_persons, 
         non_indig_females, non_indig_males, not_stated_indig_persons, 
         not_stated_indig_males, not_stated_indig_females, 
         born_in_australia, born_overseas, country_not_stated, 
         separate_house, flat_or_unit, housing_other_or_not_stated, semi_or_townhouse, 
         dwelling_owned_outright, dwelling_owned_mortgage, dwelling_other_or_not_stated,
         dwelling_rented, sa2_id) %>% 
  group_by(sa2_id) %>% 
  summarise_all(sum, na.rm = TRUE) %>% 
  mutate(percent_female = female/persons,
         percent_defacto = defacto_persons/persons,
         percent_married = married_persons/persons,
         percent_indig = indig_persons/persons,
         percent_born_in_australia = born_in_australia/persons,
         percent_unit = flat_or_unit/n_dwellings,
         percent_mortgage = dwelling_owned_mortgage/n_dwellings,
         percent_rent = dwelling_rented/n_dwellings)
```


So what I need is weighted demographic data for each of the polling places based on the number of people from each SLA2 who voted at the polling place. Since we don't know who voted where, and who can vote at all, we are making the naive assumptions that 
* Voters at each SLA are similar
* Voters are representitive of census respondents at the SLA2 level.

Download and load polling places by SA1
```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}

download.file('https://www.aec.gov.au/Elections/Federal_Elections/2016/files/polling-place-by-sa1s-2016.xlsx', './Data/polling-place-by-sa1s-2016.xlsx', method = 'libcurl')

polling_place_data <- readxl::read_xlsx('./Data/polling-place-by-sa1s-2016.xlsx')
```


Aggregate polling place data to SA2

```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_sa2 <- polling_place_data %>% 
  mutate(sa2_id = floor(SA1_id / 100)) %>% 
  group_by(year, state_ab, div_nm, pp_id, pp_nm, sa2_id) %>% 
  summarise(votes = sum(votes))

```

Combine with demographic data and aggregate

```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_demog <- polling_place_sa2 %>% 
  mutate(sa2_id = as.character(sa2_id)) %>% 
  inner_join(census_2016_all_vars)

polling_place_demog_means <- polling_place_demog %>% 
  select(year, state_ab, div_nm, pp_id, pp_nm, sa2_id, votes,
         median_age, median_household_income, average_household_size, 
         persons_per_bedroom, median_weekly_rent, median_annual_mortgage,
         percent_female, percent_defacto, percent_married, percent_indig, 
         percent_born_in_australia, percent_unit, percent_mortgage, percent_rent) %>% 
  group_by(year, state_ab, div_nm, pp_id, pp_nm) %>% 
  summarise_at(vars(median_age, median_household_income, 
                    average_household_size, persons_per_bedroom, median_weekly_rent, 
                    median_annual_mortgage, percent_female, percent_defacto, 
                    percent_married, percent_indig, percent_born_in_australia,
                    percent_unit, percent_mortgage, 
                    percent_rent), funs(weighted.mean(., w=votes)))
```
Add in 2pp at the polling booth level
```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
election_2pp <- twoparty_pollingbooth_download() 

```
```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp <- polling_place_demog_means %>% 
  group_by() %>% 
  rename(StateAb = state_ab,
         DivisionNm = div_nm,
         PollingPlace = pp_nm,
         PollingPlaceID = pp_id) %>% 
  mutate(DivisionNm = toupper(DivisionNm),
         PollingPlace = toupper(PollingPlace)) %>% 
  left_join(election_2pp %>% 
              filter(year == 2016))
```

Check for missing data

```{r}
polling_place_2pp %>% 
  summarise_all(funs(sum(is.na(.))))
```
Which booths are null?

```{r}
polling_place_2pp %>% 
  filter(is.na(TotalVotes)) %>% 
  tabyl(PollingPlace)
```

So the Absent, Postals, Pre-Poll and Provisional votes aren't in this table. Let's come back to those... 

```{r}
polling_place_2pp %>% 
  summarise_all(funs(sum(is.null(.))))
```

Remove the rows with NAs

```{r}
polling_place_2pp_clean <- polling_place_2pp %>% 
  filter(!is.na(TotalVotes))
```

```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>% 
  summarise_all(funs(sum(is.na(.))))
```

Which polling stations have missing data? Not too concerned about post code, as there are some special booths

```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>% 
  filter(is.na(median_age)|is.na(Latitude))
```
Looks like mobile teams and prepoll centres, and only latitude and longitude. Will remove the Brand mobile team, as the demographic data does not look valid.

```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean<- polling_place_2pp_clean %>% 
  dplyr::filter(PollingPlaceID != 65161)
```



# Visualising Basic Statistics
```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>% 
  ggplot(aes(x = LNP_Percent/100)) + stat_density(geom="line", colour = 'blue') +
  theme_classic(base_size = 16) +
  scale_x_continuous(labels=scales::percent) +
  labs(title = '2016 Election: LNP 2 Party Preferred Percentage', x = '2PP Percentage', 
       y = 'Density', subtitle = 'by Polling Booth, Unweighted')
```

```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>% 
  ggplot(aes(x = ALP_Percent/100)) + stat_density(geom="line", colour = 'red') +
  theme_classic(base_size = 16) +
  scale_x_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', x = '2PP Percentage', 
       y = 'Density', subtitle = 'by Polling Booth, Unweighted')
```

```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>% 
  ggplot(aes(x = Swing/100)) + stat_density(geom="line", colour = 'purple') +
  theme_classic(base_size = 16) +
  scale_x_continuous(labels=scales::percent) +
  labs(title = '2016 Election: Swing to Incumbent', x = 'Swing', 
       y = 'Density', subtitle = 'by Polling Booth, Unweighted')
```

```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>% 
  ggplot(aes(x = median_household_income)) + stat_density(geom="line", colour = 'purple') +
  theme_classic(base_size = 16) +
  scale_x_continuous(labels=scales::dollar) +
  labs(title = '2016 Census: Median Income', x = 'Median Income', 
       y = 'Density', subtitle = 'by Polling Booth, Unweighted')
```

## State Breakdowns

Can we look at these distributions by state?

```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>% 
  ggplot(aes(x = ALP_Percent/100, colour = StateAb)) + 
  stat_density(geom="line", position = 'dodge') +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  scale_x_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', x = '2PP Percentage', 
       y = 'Frequency', subtitle = 'by Polling Booth, Unweighted')
```

What about by median income

```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>%
  ggplot(aes(y = ALP_Percent/100, x = median_household_income, colour = StateAb)) +
  geom_point() +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::dollar) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', x = 'Booth Median Income',
       y = 'ALP 2pp Percentage', subtitle = 'by Polling Booth, Unweighted') +
  facet_wrap(~StateAb, nrow = 4)
```

What about comparing NSW electorates? There seems to be an odd separation in income bands for low ALP 2pp. Could this be a regional vs city difference? 

```{r, error=F, message=F, warning=F}
fp_booth_16 <- firstpref_pollingbooth_download() %>% 
  filter(year == 2016)
```


```{r, error=F, message=F, warning=F}
polling_2cp <- read_csv('https://results.aec.gov.au/20499/Website/Downloads/HouseTcpByCandidateByPollingPlaceDownload-20499.csv', skip = 1)

polling_2pp <- read_csv('https://results.aec.gov.au/20499/Website/Downloads/HouseTppByPollingPlaceDownload-20499.csv', skip = 1)

fp_booth_2016 <- read_csv('https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-NSW.csv', skip = 1) %>% 
  rbind(read_csv('https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-VIC.csv', skip = 1)) %>% 
  rbind(read_csv('https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-QLD.csv', skip = 1)) %>% 
  rbind(read_csv('https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-SA.csv', skip = 1)) %>% 
  rbind(read_csv('https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-WA.csv', skip = 1)) %>% 
  rbind(read_csv('https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-TAS.csv', skip = 1)) %>% 
  rbind(read_csv('https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-NT.csv', skip = 1)) %>% 
  rbind(read_csv('https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-ACT.csv', skip = 1))
```

```{r}
coalition_contest_2016 <- fp_booth_2016 %>% 
  group_by(DivisionNm, PartyNm, HistoricElected) %>% 
  summarise(OrdinaryVotes = sum(OrdinaryVotes)) %>%
  filter(PartyNm %in% c('Liberal', 'Country Liberals (NT)',
                        'Liberal National Party of Queensland',
                        'The Nationals')) %>% 
  group_by(DivisionNm) %>% 
  top_n(1) %>% 
  select(DivisionNm, PartyNm)
```

If we look at a couple of the states where high income booths tend to vote strongly for the coalition as well as lower income booths, we can see that some (but not all) of the lower income booths are contested by The Nationals. This indicares (not surprisingly) that Nationals voters and Liberal voters are different socio-economically, or possibly that city and country coalition voters differ. 
```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>% 
  mutate(DivisionNm = stringr::str_to_title(DivisionNm)) %>%
  inner_join(coalition_contest_2016) %>% 
  filter(StateAb == 'NSW') %>% 
  ggplot(aes(y = ALP_Percent/100, x = median_household_income, colour = PartyNm)) +
  geom_point(size = 3) +
  theme_classic(base_size = 16) + scale_color_manual(values = c('blue', 'dark green')) +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::dollar) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', x = 'Booth Median Income',
       y = 'ALP 2pp Percentage', colour = 'Coalition Party', 
       subtitle = 'by Polling Booth, Unweighted (NSW)') 
```


```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>% 
  mutate(DivisionNm = stringr::str_to_title(DivisionNm)) %>%
  inner_join(coalition_contest_2016) %>% 
  filter(StateAb == 'VIC') %>% 
  ggplot(aes(y = ALP_Percent/100, x = median_household_income, colour = PartyNm)) +
  geom_point(size = 3) +
  theme_classic(base_size = 16) + scale_color_manual(values = c('blue', 'dark green')) +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::dollar) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', x = 'Booth Median Income',
       y = 'ALP 2pp Percentage', colour = 'Coalition Party', 
       subtitle = 'by Polling Booth, Unweighted (VIC)') 
```

This effect is less clear in states where the Nationals aren't as prominent, either because the Nationals aren't as prominent (SA, WA, TAS), or are merged with the Liberals (QLD).  
```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>% 
  mutate(DivisionNm = stringr::str_to_title(DivisionNm)) %>%
  inner_join(coalition_contest_2016) %>% 
  filter(StateAb == 'WA') %>% 
  ggplot(aes(y = ALP_Percent/100, x = median_household_income, colour = PartyNm)) +
  geom_point(size = 3) +
  theme_classic(base_size = 16) + scale_color_manual(values = c('blue', 'dark green')) +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::dollar) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', x = 'Booth Median Income',
       y = 'ALP 2pp Percentage', colour = 'Coalition Party', 
       subtitle = 'by Polling Booth, Unweighted (WA)') 
```


Perhaps we would be better off using the geographical classifications from the AEC.

```{r}
library(rvest)

webpage <- read_html("http://results.aec.gov.au/20499/Website/HouseDivisionClassifications-20499-NAT.htm")

Division_Classifications <- webpage %>%
  html_nodes("#divisionClassifications") %>% 
  html_table(fill = TRUE) %>%
  .[[1]]
Division_Classifications <- Division_Classifications %>% 
  filter(Division != 'Total Enrolment')
```

The graph below shows that the booths that have a low ALP 2pp and a low median income are primarily rural booths. This relationship seems stronger than the Lib/Nat split.
```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  filter(StateAb == 'NSW') %>% 
  ggplot(aes(y = ALP_Percent/100, x = median_household_income, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::dollar) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', 
       y = 'ALP 2pp Percentage',
       x = 'Booth Median Income', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted (NSW)')
```

Looking at all states we see a similar relationship, although less strong than in NSW.
```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  ggplot(aes(y = ALP_Percent/100, x = median_household_income, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::dollar) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', y = 'ALP 2pp Percentage',
       x = 'Median Booth Income', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted')
```

What about some of the other variables?

```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  ggplot(aes(y = ALP_Percent/100, x = average_household_size, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', y = 'ALP 2pp Percentage',
       x = 'Average Household Size', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted')
```

```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  ggplot(aes(y = ALP_Percent/100, x = percent_female, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_x_continuous(labels=scales::percent) +
  scale_y_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', y = 'ALP 2pp Percentage',
       x = 'Percent Female', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted')
```

```{r}
polling_place_2pp_clean %>% 
  select(ALP_Percent, Swing, median_age, median_household_income, average_household_size,
         persons_per_bedroom, median_weekly_rent, median_annual_mortgage,
         percent_female, percent_defacto, percent_born_in_australia,
         percent_unit, percent_mortgage, percent_rent) %>% 
  cor %>% 
  kable()
```

```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  ggplot(aes(y = ALP_Percent/100, x = persons_per_bedroom, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', y = 'ALP 2pp Percentage',
       x = 'Persons per Bedroom', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted')
```

```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  ggplot(aes(y = ALP_Percent/100, x = percent_unit, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', y = 'ALP 2pp Percentage',
       x = 'Percent of Dwellings - Unit', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted')
```

```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  ggplot(aes(y = ALP_Percent/100, x = percent_mortgage, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', y = 'ALP 2pp Percentage',
       x = 'Percent of Dwellings - Under Mortgage', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted')
```

```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  ggplot(aes(y = ALP_Percent/100, x = percent_rent, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', y = 'ALP 2pp Percentage',
       x = 'Percent of Dwellings - Renting', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted')
```

```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  ggplot(aes(y = ALP_Percent/100, x = percent_indig, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', y = 'ALP 2pp Percentage',
       x = 'Percent Indigeneous', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted')
```

```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  ggplot(aes(y = ALP_Percent/100, x = percent_born_in_australia, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', y = 'ALP 2pp Percentage',
       x = 'Percent Born in Australia', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted')
```

```{r, fig.width=4, fig.height=4, error=F, message=F, warning=F}
polling_place_2pp_clean %>% 
  mutate(Division = stringr::str_to_title(DivisionNm)) %>%
  inner_join(Division_Classifications) %>% 
  ggplot(aes(y = ALP_Percent/100, x = percent_defacto, colour = Demographic)) +
  geom_point(size = 1) +
  theme_classic(base_size = 16) + scale_color_brewer(palette = "Dark2") +
  theme(legend.position = 'bottom') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(labels=scales::percent) +
  labs(title = '2016 Election: ALP 2 Party Preferred Percentage', y = 'ALP 2pp Percentage',
       x = 'Percent in a Defacto Relationship', colour = 'Region',
       subtitle = 'by Polling Booth, Unweighted')
```


































